Multiobjective evolutionary algorithms have incorporated surrogate models in order to
reduce the number of required evaluations to approximate the Pareto front of …

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for
computationally expensive optimization problems with more than three objectives. The …

Abstract Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of
optimisers developed to undertake problems with computationally expensive fitness …

The use of surrogate models is a standard method for dealing with complex real-world
optimization problems. The first surrogate models were applied to continuous optimization …

Some data mining, AI and data processing tasks might have data loss whose
estimation/imputation is an important problem to be solved. Genetic algorithms are efficient …

Complexity in solving real‐world multicriteria optimization problems often stems from the fact
that complex, expensive, and/or time‐consuming simulation tools or physical experiments …

Although researchers have successfully incorporated metamodels in evolutionary
algorithms to solve computational-expensive optimization problems, they have scarcely …

Expensive dynamic multi-objective optimization problems (EDMOPs) is one kind of DMOPs
where the objectives change over time and the function evaluations commonly involve …

In this paper, a Pareto front (PF)-based sampling algorithm, PF-Voronoi sampling method, is
proposed to solve computationally intensive multi-objective problems of medium size. The …

In this paper, a multi/many-objective optimization algorithm assisted by radial basis function
is proposed based on reference vectors to solve computationally expensive optimization …